聚类算法（Affinity Propagation, AP）是一种基于消息传递的聚类方法，能够自动确定聚类的数量。虽然Objective-C并不是数据科学中常用的语言，但我们可以实现一个简单的AP算法的示例。
Objective-C 聚类AP算法示例
#include #include #include // 定义数据点的结构 struct DataPoint { double x; double y; double value; float clusterID; float affinity; }; // 初始化数据点 void initPoints(struct DataPoint *points, int count) { for (int i = 0; i < count; i++) { points[i].x = (double)rand() / (double)RAND_MAX; points[i].y = (double)rand() / (double)RAND_MAX; points[i].value = (double)rand() / (double)RAND_MAX; points[i].clusterID = 0; points[i].affinity = 0; } } // 计算数据点的相似度 float computeAffinity(struct DataPoint *point1, struct DataPoint *point2) { float affinity = 0; // 简单计算相似度，根据值域的相似性 affinity = fabsf(point1.value - point2.value); return affinity; } // 消息传递过程 void propagate(struct DataPoint *dataPoints) { int n = sizeof(dataPoints) / sizeof(struct DataPoint); float maxAffinity = 0; float *newAffinities = malloc(n * sizeof(float)); // 初始化最大相似度 for (int i = 0; i < n; i++) { newAffinities[i] = 0; } // 第一轮消息传递 for (int i = 0; i < n; i++) { struct DataPoint *current = dataPoints + i; float currentAffinity = 0; // 消息传递到其他点 for (int j = 0; j < n; j++) { if (i != j) { struct DataPoint *neighbor = dataPoints + j; float affinity = computeAffinity(current, neighbor); if (affinity > currentAffinity) { currentAffinity = affinity; } } } // 更新最大相似度 if (currentAffinity > maxAffinity) { maxAffinity = currentAffinity; } // 更新新相似度 newAffinities[i] = currentAffinity; } // 更新数据点的聚类信息 for (int i = 0; i < n; i++) { struct DataPoint *dataPoint = dataPoints + i; dataPoint->affinity = newAffinities[i]; } free(newAffinities); } // 主函数 int main(int argc, char **argv) { // 初始化数据点 struct DataPoint *dataPoints = malloc(100 * sizeof(struct DataPoint)); initPoints(dataPoints, 100); // 进行消息传递 propagate(dataPoints); // 输出结果 printf("聚类完成!\n"); return 0; }